Antifragile Workflows
What if AI workflows could get stronger from failures, not just survive them?
"Some things benefit from shocks; they thrive and grow when exposed to volatility, randomness, disorder, and stressors."
The Core Idea
Most AI workflow systems are designed to be robust — they try to resist failure and maintain stability. But robustness has limits. A robust system can only handle failures up to its design threshold.
Antifragile systems go beyond robustness. They actually improve when subjected to stressors. Every failure teaches the system something, making it better prepared for the next challenge.
# The Antifragility Spectrum
Fragile → Breaks under stress
Robust → Resists stress (to a point)
Antifragile → Gains from stress
Current AI Workflows: Mostly Fragile
Our Goal: Move toward AntifragilePrinciples We're Exploring
Embrace Optionality
Multiple providers, multiple models, multiple paths. When one fails, others are ready.
Small Failures, Big Learning
Expose the system to small, contained failures to build resilience against large ones.
Redundancy Without Bloat
Strategic redundancy in critical paths, not everywhere. Cost-effective resilience.
Skin in the Game
Agents that propose actions should face consequences of their failures (budget penalties, scope reduction).
What This Might Look Like in Practice
# Hypothetical antifragile workflow behavior:
This is fundamentally different from a simple retry-with-fallback mechanism. The system is learning and adapting based on its experience with failures.
Honest Assessment: This Is Hard
We're not claiming to have solved this. True antifragility in AI systems is an open research problem. Here's where we are:
- Multi-provider support — Optionality is built in
- Scope isolation — Failures are contained
- Learning from failures — In design phase
- Controlled stress exposure — Researching approaches
Interested in resilient AI systems?
We'd love to hear your ideas and experiences.